There exists a lot of different algorithms families that can be called "metaheuristics", stricly speaking, there are a very, very, very large number of metaheuristics instances.

Defining what is a metaheuristic "family" is a difficult problem: when may I called this or this algorithm an evolutionary one? Is estimation of distribution a sub-family of genetic algorithms? What is the difference between ant colony optimization and stochastic gradient ascent? Etc.

Despite the difficulty of classifying metaheuristics, there is some interesting characteristics shared by stochastic metaheuristics. Indeed, they are all iteratively manipulating a sample of the objective function[1]

For example, simulated annealing is often depicted as a probabilistic descent algorithm, but it is more than that. Indeed, simulated annealing is based on the Metropolis-Hastings algorithm, which is a way of sampling any probability distributionn, as long as you can calculate its density at any point. Thus, simulated annealing use an approximation of the objective function as a probability density function to generate a sampling. It is even more obvious if you consider a step by step decrease of the temperature. Estimation of distribution are another obvious example: they are explicitly manipulating samplings, but one can also have the same thoughts about evolutionary algorithms, even if they are manipulating the sampling rather implicitely.

The diagram tries to illustrate this idea: (a) a descent algorithm can have the same sampling behaviour than an iteration of a (b) "population" method.

Given these common processes, is it possible to design a kind of "universal" metaheuristic ? Theoretically, the answer is yes. For example, in the continuous domain, consider an estimation of distribution algorithm, using a mixture of gaussian kernel: it can learn any probability density function (possibly needing an infinite number of kernels). Thus, carefully choosing the function to use at each iteration and the selection operator, one can reproduce the behaviour of any stochastic metaheuristic.

Of course, choosing the correct mixture (and the other parameters) is a very difficult problem in practice. But I find interesting the idea that the problem of designing a metaheuristic can be reduced to a configuration problem.

Notes

[1] Johann Dréo, Patrick Siarry, "Stochastic metaheuristics as sampling techniques using swarm intelligence. ", in "Swarm Intelligence: Focus on Ant and Particle Swarm Optimization", Felix T. S. Chan, Manoj Kumar Tiwari (Eds.), Advanced Robotic Systems International, I-Tech Education and Publishing, Vienna, Austria , ISBN 978-3-902613-09-7 - December 2008